{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:41:24Z","timestamp":1755823284434,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":44,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T00:00:00Z","timestamp":1698278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,26]]},"DOI":"10.1145\/3581783.3611748","type":"proceedings-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T07:27:40Z","timestamp":1698391660000},"page":"8376-8386","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Up to Thousands-fold Storage Saving: Towards Efficient Data-Free Distillation of Large-Scale Visual Classifiers"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9431-6414","authenticated-orcid":false,"given":"Fanfan","family":"Ye","sequence":"first","affiliation":[{"name":"Zhejiang University & Hikvision Research Institute, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8959-6266","authenticated-orcid":false,"given":"Bingyi","family":"Lu","sequence":"additional","affiliation":[{"name":"Shanghai Jiaotong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4228-258X","authenticated-orcid":false,"given":"Liang","family":"Ma","sequence":"additional","affiliation":[{"name":"Hikvision Research Institute, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5944-1818","authenticated-orcid":false,"given":"Qiaoyong","family":"Zhong","sequence":"additional","affiliation":[{"name":"Hikvision Research Institute, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8065-5901","authenticated-orcid":false,"given":"Di","family":"Xie","sequence":"additional","affiliation":[{"name":"Hikvision Research Institute, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,10,27]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Karianto Leman, and Tulika Mitra.","author":"Binici Kuluhan","year":"2022","unstructured":"Kuluhan Binici, Shivam Aggarwal, Nam Trung Pham, Karianto Leman, and Tulika Mitra. 2022a. Robust and Resource-Efficient Data-Free Knowledge Distillation by Generative Pseudo Replay. arXiv preprint arXiv:2201.03019 (2022)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV51458.2022.00368"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01318"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00361"},{"key":"e_1_3_2_1_5_1","first-page":"14835","article-title":"Qimera: Data-free quantization with synthetic boundary supporting samples","volume":"34","author":"Choi Kanghyun","year":"2021","unstructured":"Kanghyun Choi, Deokki Hong, Noseong Park, Youngsok Kim, and Jinho Lee. 2021. Qimera: Data-free quantization with synthetic boundary supporting samples. Advances in Neural Information Processing Systems, Vol. 34 (2021), 14835--14847.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00363"},{"key":"e_1_3_2_1_7_1","volume-title":"Advances in Neural Information Processing Systems","volume":"30","author":"Vries Harm De","year":"2017","unstructured":"Harm De Vries, Florian Strub, J\u00e9r\u00e9mie Mary, Hugo Larochelle, Olivier Pietquin, and Aaron C Courville. 2017. Modulating early visual processing by language. Advances in Neural Information Processing Systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_1_9_1","volume-title":"Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation. arXiv preprint arXiv:2209.10359","author":"Do Kien","year":"2022","unstructured":"Kien Do, Hung Le, Dung Nguyen, Dang Nguyen, Haripriya Harikumar, Truyen Tran, Santu Rana, and Svetha Venkatesh. 2022. Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation. arXiv preprint arXiv:2209.10359 (2022)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i6.20613"},{"key":"e_1_3_2_1_11_1","volume-title":"Data-free adversarial distillation. arXiv preprint arXiv:1912.11006","author":"Fang Gongfan","year":"2019","unstructured":"Gongfan Fang, Jie Song, Chengchao Shen, Xinchao Wang, Da Chen, and Mingli Song. 2019. Data-free adversarial distillation. arXiv preprint arXiv:1912.11006 (2019)."},{"key":"e_1_3_2_1_12_1","volume-title":"Contrastive model inversion for data-free knowledge distillation. arXiv preprint arXiv:2105.08584","author":"Fang Gongfan","year":"2021","unstructured":"Gongfan Fang, Jie Song, Xinchao Wang, Chengchao Shen, Xingen Wang, and Mingli Song. 2021. Contrastive model inversion for data-free knowledge distillation. arXiv preprint arXiv:2105.08584 (2021)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00201"},{"key":"e_1_3_2_1_15_1","unstructured":"Geoffrey Hinton Oriol Vinyals Jeff Dean et al. 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 Vol. 2 7 (2015)."},{"key":"e_1_3_2_1_16_1","volume-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861","author":"Howard Andrew G","year":"2017","unstructured":"Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00577"},{"key":"e_1_3_2_1_18_1","first-page":"23505","article-title":"Rebooting acgan: Auxiliary classifier gans with stable training","volume":"34","author":"Kang Minguk","year":"2021","unstructured":"Minguk Kang, Woohyeon Shim, Minsu Cho, and Jaesik Park. 2021. Rebooting acgan: Auxiliary classifier gans with stable training. Advances in Neural Information Processing Systems, Vol. 34 (2021), 23505--23518.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_19_1","unstructured":"Alex Krizhevsky Geoffrey Hinton et al. 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_2_1_20_1","volume-title":"Tiny imagenet visual recognition challenge. CS 231N","author":"Le Ya","year":"2015","unstructured":"Ya Le and Xuan Yang. 2015. Tiny imagenet visual recognition challenge. CS 231N, Vol. 7, 7 (2015), 3."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00437"},{"key":"e_1_3_2_1_22_1","volume-title":"Model Conversion via Differentially Private Data-Free Distillation. arXiv preprint arXiv:2304.12528","author":"Liu Bochao","year":"2023","unstructured":"Bochao Liu, Pengju Wang, Shikun Li, Dan Zeng, and Shiming Ge. 2023. Model Conversion via Differentially Private Data-Free Distillation. arXiv preprint arXiv:2304.12528 (2023)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01611"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00156"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"e_1_3_2_1_26_1","volume-title":"Large-scale generative data-free distillation. arXiv preprint arXiv:2012.05578","author":"Luo Liangchen","year":"2020","unstructured":"Liangchen Luo, Mark Sandler, Zi Lin, Andrey Zhmoginov, and Andrew Howard. 2020. Large-scale generative data-free distillation. arXiv preprint arXiv:2012.05578 (2020)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00152"},{"key":"e_1_3_2_1_28_1","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Micaelli Paul","year":"2019","unstructured":"Paul Micaelli and Amos J Storkey. 2019. Zero-shot knowledge transfer via adversarial belief matching. Advances in Neural Information Processing Systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_29_1","volume-title":"cGANs with projection discriminator. arXiv preprint arXiv:1802.05637","author":"Miyato Takeru","year":"2018","unstructured":"Takeru Miyato and Masanori Koyama. 2018. cGANs with projection discriminator. arXiv preprint arXiv:1802.05637 (2018)."},{"volume-title":"PyTorch: An Imperative Style","author":"Paszke Adam","key":"e_1_3_2_1_30_1","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. dtextquotesingle Alch\u00e9-Buc, E. Fox, and R. Garnett (Eds.). Curran Associates, Inc., 8024--8035. http:\/\/papers.neurips.cc\/paper\/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf"},{"key":"e_1_3_2_1_31_1","volume-title":"Konda Reddy Mopuri, and Qiang Qiu","author":"Patel Gaurav","year":"2023","unstructured":"Gaurav Patel, Konda Reddy Mopuri, and Qiang Qiu. 2023. Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation. arXiv preprint arXiv:2302.14290 (2023)."},{"key":"e_1_3_2_1_32_1","volume-title":"Imagenet-21k pretraining for the masses. arXiv preprint arXiv:2104.10972","author":"Ridnik Tal","year":"2021","unstructured":"Tal Ridnik, Emanuel Ben-Baruch, Asaf Noy, and Lihi Zelnik-Manor. 2021. Imagenet-21k pretraining for the masses. arXiv preprint arXiv:2104.10972 (2021)."},{"key":"e_1_3_2_1_33_1","volume-title":"Danda Pani Paudel, and Luc Van Gool","author":"Shahbazi Mohamad","year":"2022","unstructured":"Mohamad Shahbazi, Martin Danelljan, Danda Pani Paudel, and Luc Van Gool. 2022. Collapse by conditioning: Training class-conditional GANs with limited data. arXiv preprint arXiv:2201.06578 (2022)."},{"key":"e_1_3_2_1_34_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00162"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i11.17228"},{"key":"e_1_3_2_1_37_1","volume-title":"Self-distilled Knowledge Delegator for Exemplar-free Class Incremental Learning. arXiv preprint arXiv:2205.11071","author":"Ye Fanfan","year":"2022","unstructured":"Fanfan Ye, Liang Ma, Qiaoyong Zhong, Di Xie, and Shiliang Pu. 2022. Self-distilled Knowledge Delegator for Exemplar-free Class Incremental Learning. arXiv preprint arXiv:2205.11071 (2022)."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00874"},{"key":"e_1_3_2_1_39_1","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Yoo Jaemin","year":"2019","unstructured":"Jaemin Yoo, Minyong Cho, Taebum Kim, and U Kang. 2019. Knowledge extraction with no observable data. Advances in Neural Information Processing Systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_40_1","volume-title":"Wide residual networks. arXiv preprint arXiv:1605.07146","author":"Zagoruyko Sergey","year":"2016","unstructured":"Sergey Zagoruyko and Nikos Komodakis. 2016. Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)."},{"key":"e_1_3_2_1_41_1","volume-title":"mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412","author":"Zhang Hongyi","year":"2017","unstructured":"Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. 2017. mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)."},{"key":"e_1_3_2_1_42_1","volume-title":"International conference on machine learning. PMLR, 7354--7363","author":"Zhang Han","year":"2019","unstructured":"Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena. 2019. Self-attention generative adversarial networks. In International conference on machine learning. PMLR, 7354--7363."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01540"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01202"}],"event":{"name":"MM '23: The 31st ACM International Conference on Multimedia","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Ottawa ON Canada","acronym":"MM '23"},"container-title":["Proceedings of the 31st ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581783.3611748","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3581783.3611748","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:13:11Z","timestamp":1755821591000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581783.3611748"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,26]]},"references-count":44,"alternative-id":["10.1145\/3581783.3611748","10.1145\/3581783"],"URL":"https:\/\/doi.org\/10.1145\/3581783.3611748","relation":{},"subject":[],"published":{"date-parts":[[2023,10,26]]},"assertion":[{"value":"2023-10-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}